DNETC: dynamic network embedding preserving both triadic closure evolution and community structures

Yang, Min; Chen, Xiaoliang; Chen, Baiyang; Lu, Peng; Du, Yajun

doi:10.1007/s10115-022-01792-4

Cited by 4 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach enhances the utility of community structure analysis by imposing constraints on the community member matrix, namely, by ensuring that it is non-negative and orthogonal. During the process of node embedding, Wang et al [23] successfully maintained the original structural properties and intrinsic qualities of the network. Consequently, they proposed the implementation of the Modularized Non-Negative Matrix Factorization (MNMF) model.…”

Section: Learning-model-based Community Detectionmentioning

confidence: 99%

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Huang,

Zhong

2024

Mathematics

View full text Add to dashboard Cite

Community structure is a significant characteristic of complex networks, and community detection has valuable applications in network structure analysis. Non-negative matrix factorization (NMF) is a key set of algorithms used to solve the community detection issue. Nevertheless, the localization of feature vectors in the adjacency matrix, which represents the characteristics of complex network structures, frequently leads to the failure of NMF-based approaches when the data matrix has a low density. This paper presents a novel algorithm for detecting sparse network communities using non-negative matrix factorization (NMF). The algorithm utilizes local feature vectors to represent the original network topological features and learns regularization matrices. The resulting feature matrices effectively reveal the global structure of the data matrix, demonstrating enhanced feature expression capabilities. The regularized data matrix resolves the issue of localized feature vectors caused by sparsity or noise, in contrast to the adjacency matrix. The approach has superior accuracy in detecting community structures compared to standard NMF-based community detection algorithms, as evidenced by experimental findings on both simulated and real-world networks.

show abstract

Section: Learning-model-based Community Detectionmentioning

confidence: 99%

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Huang,

Zhong

2024

Mathematics

View full text Add to dashboard Cite

show abstract

Bi-DNE: bilayer evolutionary pattern preserved embedding for dynamic networks

Gu,

Chen,

Yang

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Network embedding is a technique used to generate low-dimensional vectors representing each node in a network while maintaining the original topology and properties of the network. This technology enables a wide range of learning tasks, including node classification and link prediction. However, the current landscape of network embedding approaches predominantly revolves around static networks, neglecting the dynamic nature that characterizes real social networks. Dynamics at both the micro- and macrolevels are fundamental drivers of network evolution. Microlevel dynamics provide a detailed account of the network topology formation process, while macrolevel dynamics reveal the evolutionary trends of the network. Despite recent dynamic network embedding efforts, a few approaches accurately capture the evolution patterns of nodes at the microlevel or effectively preserve the crucial dynamics of both layers. Our study introduces a novel method for embedding networks, i.e., bilayer evolutionary pattern-preserving embedding for dynamic networks (Bi-DNE), that preserves the evolutionary patterns at both the micro- and macrolevels. The model utilizes strengthened triadic closure to represent the network structure formation process at the microlevel, while a dynamic equation constrains the network structure to adhere to the densification power-law evolution pattern at the macrolevel. The proposed Bi-DNE model exhibits significant performance improvements across a range of tasks, including link prediction, reconstruction, and temporal link analysis. These improvements are demonstrated through comprehensive experiments carried out on both simulated and real-world dynamic network datasets. The consistently superior results to those of the state-of-the-art methods provide empirical evidence for the effectiveness of Bi-DNE in capturing complex evolutionary patterns and learning high-quality node representations. These findings validate the methodological innovations presented in this work and mark valuable progress in the emerging field of dynamic network representation learning. Further exploration demonstrates that Bi-DNE is sensitive to the analysis task parameters, leading to a more accurate representation of the natural evolution process during dynamic network embedding.

show abstract

TemporalHAN: Hierarchical attention-based heterogeneous temporal network embedding

Mo,

Wan,

Tang

2024

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

DNETC: dynamic network embedding preserving both triadic closure evolution and community structures

Cited by 4 publications

References 43 publications

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Bi-DNE: bilayer evolutionary pattern preserved embedding for dynamic networks

TemporalHAN: Hierarchical attention-based heterogeneous temporal network embedding

Contact Info

Product

Resources

About